# treemind
`treemind` is a high-performance library for interpreting tree-based models. It supports regression, binary and multiclass classification, and handles both numerical and categorical features. By analyzing split intervals and feature interactions, `treemind` helps you understand which features drive predictions and how they interact making it ideal for model explanation, debugging, and auditing.
> A formal research paper detailing the theoretical foundation of `treemind` is forthcoming.
---
## Installation
Install `treemind` via pip:
```bash
pip install treemind
```
---
## Key Features
* **Feature Analysis**
Quantifies how individual features influence predictions across specific decision boundaries.
* **Interaction Detection**
Detects and visualizes interaction effects between two or more features at any order `n`, constrained by memory and time.
* **Optimized Performance**
Fast even on deep models thanks to efficient Cython-backed core.
* **Rich Visualizations**
Interactive and static plots to visualize importance, split intervals, and interaction strength.
* **Broad Model Support**
Compatible with `xgboost`, `lightgbm`, `catboost`, `sklearn`, and `perpetual`. Works with regression, binary, and multiclass tasks. Supports categorical features.
---
## Algorithm & Performance
The `treemind` algorithm analyzes how often features and their combinations appear in decision paths, then summarizes their behavior over split intervals.
* [Algorithm Overview](https://treemind.readthedocs.io/en/latest/algorithm.html)
* [Performance Benchmarks](https://treemind.readthedocs.io/en/latest/experiments/experiment_main.html)
---
### Quickstart Example
This walkthrough shows how to use `treemind.Explainer` with a LightGBM model trained on the Breast Cancer dataset.
```python
from lightgbm import LGBMClassifier
from sklearn.datasets import load_breast_cancer
from treemind import Explainer
from treemind.plot import (
feature_plot,
interaction_plot,
interaction_scatter_plot,
importance_plot,
)
# Load sample data
X, y = load_breast_cancer(return_X_y=True, as_frame=True)
# Train a model
model = LGBMClassifier(verbose=-1)
model.fit(X, y)
# Create an explainer
explainer = Explainer(model)
```
---
### Count Feature Appearances
To see how often each feature (or feature pair) appears in the decision trees:
```python
explainer.count_node(degree=1) # Individual feature usage
```
```text
| column_index | count |
|--------------|-------|
| 21 | 1739 |
| 27 | 1469 |
```
```python
explainer.count_node(degree=2) # Pairwise feature usage
```
```text
| column1_index | column2_index | count |
|---------------|---------------|-------|
| 21 | 22 | 927 |
| 21 | 23 | 876 |
```
---
### One-Dimensional Feature Analysis
Analyze how a single feature influences the model:
```python
result1_d = explainer.explain(degree=1)
```
Inspect a specific feature (e.g., feature 21):
```python
result1_d[21]
```
```text
| worst_texture_lb | worst_texture_ub | value | std | count |
|------------------|------------------|-----------|----------|--------|
| -inf | 18.460 | 3.185128 | 8.479232 | 402.24 |
| 18.460 | 19.300 | 3.160656 | 8.519873 | 402.39 |
```
#### Feature Visualization
```python
feature_plot(result1_d, 21)
```
<p align="center">
<img src="/docs/source/_static/api/feature_plot.png" alt="Feature Plot" width="80%">
</p>
#### Feature Importance
```python
result1_d.importance()
```
```text
| feature_0 | importance |
|----------------------|------------|
| worst_concave_points | 2.326004 |
| worst_perimeter | 2.245493 |
```
```python
importance_plot(result1_d)
```
<p align="center">
<img src="/docs/source/_static/api/importance_plot.png" alt="Feature Importance" width="80%">
</p>
---
### Two-Dimensional Interaction Analysis
Evaluate how two features interact to influence predictions:
```python
result2_d = explainer.explain(degree=2)
result2_d[21, 22]
```
```text
| worst_texture_lb | worst_texture_ub | worst_concave_points_lb | worst_concave_points_ub | value | std | count |
|------------------|------------------|--------------------------|--------------------------|----------|----------|--------|
| -inf | 18.46 | -inf | 0.058860 | 4.929324 | 7.679424 | 355.40 |
```
#### Interaction Importance
```python
result2_d.importance()
```
```text
| feature_0 | feature_1 | importance |
|------------------|----------------------|------------|
| worst_perimeter | worst_area | 2.728454 |
| worst_texture | worst_concave_points | 2.439605 |
```
```python
importance_plot(result2_d)
```
<p align="center">
<img src="/docs/source/_static/api/importance_plot2d.png" alt="2D Importance" width="80%">
</p>
#### Interaction Plots
```python
interaction_plot(result2_d, (21, 22))
```
<p align="center">
<img src="/docs/source/_static/api/interaction_plot.png" alt="Interaction Plot" width="80%">
</p>
```python
interaction_scatter_plot(X, result2_d, (21, 22))
```
<p align="center">
<img src="/docs/source/_static/api/interaction_scatter_plot.png" alt="Interaction Scatter" width="80%">
</p>
---
## Contributing
Contributions are welcome! If you'd like to improve `treemind` or suggest new features, feel free to fork the repository and submit a pull request.
---
## License
`treemind` is released under the MIT License. See the [LICENSE](./LICENSE) file for details.
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"description": "\n# treemind\n\n`treemind` is a high-performance library for interpreting tree-based models. It supports regression, binary and multiclass classification, and handles both numerical and categorical features. By analyzing split intervals and feature interactions, `treemind` helps you understand which features drive predictions and how they interact making it ideal for model explanation, debugging, and auditing.\n\n\n> A formal research paper detailing the theoretical foundation of `treemind` is forthcoming.\n\n---\n\n## Installation\n\nInstall `treemind` via pip:\n\n```bash\npip install treemind\n```\n\n---\n\n## Key Features\n\n* **Feature Analysis**\n Quantifies how individual features influence predictions across specific decision boundaries.\n\n* **Interaction Detection**\n Detects and visualizes interaction effects between two or more features at any order `n`, constrained by memory and time.\n\n* **Optimized Performance**\n Fast even on deep models thanks to efficient Cython-backed core.\n\n* **Rich Visualizations**\n Interactive and static plots to visualize importance, split intervals, and interaction strength.\n\n* **Broad Model Support**\n Compatible with `xgboost`, `lightgbm`, `catboost`, `sklearn`, and `perpetual`. Works with regression, binary, and multiclass tasks. Supports categorical features.\n\n---\n\n## Algorithm & Performance\n\nThe `treemind` algorithm analyzes how often features and their combinations appear in decision paths, then summarizes their behavior over split intervals.\n\n* [Algorithm Overview](https://treemind.readthedocs.io/en/latest/algorithm.html)\n* [Performance Benchmarks](https://treemind.readthedocs.io/en/latest/experiments/experiment_main.html)\n\n---\n\n### Quickstart Example\n\nThis walkthrough shows how to use `treemind.Explainer` with a LightGBM model trained on the Breast Cancer dataset.\n\n```python\nfrom lightgbm import LGBMClassifier\nfrom sklearn.datasets import load_breast_cancer\n\nfrom treemind import Explainer\nfrom treemind.plot import (\n feature_plot,\n interaction_plot,\n interaction_scatter_plot,\n importance_plot,\n)\n\n# Load sample data\nX, y = load_breast_cancer(return_X_y=True, as_frame=True)\n\n# Train a model\nmodel = LGBMClassifier(verbose=-1)\nmodel.fit(X, y)\n\n# Create an explainer\nexplainer = Explainer(model)\n```\n\n---\n\n### Count Feature Appearances\n\nTo see how often each feature (or feature pair) appears in the decision trees:\n\n```python\nexplainer.count_node(degree=1) # Individual feature usage\n```\n\n```text\n| column_index | count |\n|--------------|-------|\n| 21 | 1739 |\n| 27 | 1469 |\n```\n\n```python\nexplainer.count_node(degree=2) # Pairwise feature usage\n```\n\n```text\n| column1_index | column2_index | count |\n|---------------|---------------|-------|\n| 21 | 22 | 927 |\n| 21 | 23 | 876 |\n```\n\n---\n\n### One-Dimensional Feature Analysis\n\nAnalyze how a single feature influences the model:\n\n```python\nresult1_d = explainer.explain(degree=1)\n```\n\nInspect a specific feature (e.g., feature 21):\n\n```python\nresult1_d[21]\n```\n\n```text\n| worst_texture_lb | worst_texture_ub | value | std | count |\n|------------------|------------------|-----------|----------|--------|\n| -inf | 18.460 | 3.185128 | 8.479232 | 402.24 |\n| 18.460 | 19.300 | 3.160656 | 8.519873 | 402.39 |\n```\n\n#### Feature Visualization\n\n```python\nfeature_plot(result1_d, 21)\n```\n\n<p align=\"center\">\n <img src=\"/docs/source/_static/api/feature_plot.png\" alt=\"Feature Plot\" width=\"80%\">\n</p>\n\n#### Feature Importance\n\n```python\nresult1_d.importance()\n```\n\n```text\n| feature_0 | importance |\n|----------------------|------------|\n| worst_concave_points | 2.326004 |\n| worst_perimeter | 2.245493 |\n```\n\n```python\nimportance_plot(result1_d)\n```\n\n<p align=\"center\">\n <img src=\"/docs/source/_static/api/importance_plot.png\" alt=\"Feature Importance\" width=\"80%\">\n</p>\n\n---\n\n### Two-Dimensional Interaction Analysis\n\nEvaluate how two features interact to influence predictions:\n\n```python\nresult2_d = explainer.explain(degree=2)\nresult2_d[21, 22]\n```\n\n```text\n| worst_texture_lb | worst_texture_ub | worst_concave_points_lb | worst_concave_points_ub | value | std | count |\n|------------------|------------------|--------------------------|--------------------------|----------|----------|--------|\n| -inf | 18.46 | -inf | 0.058860 | 4.929324 | 7.679424 | 355.40 |\n```\n\n#### Interaction Importance\n\n```python\nresult2_d.importance()\n```\n\n```text\n| feature_0 | feature_1 | importance |\n|------------------|----------------------|------------|\n| worst_perimeter | worst_area | 2.728454 |\n| worst_texture | worst_concave_points | 2.439605 |\n```\n\n```python\nimportance_plot(result2_d)\n```\n\n<p align=\"center\">\n <img src=\"/docs/source/_static/api/importance_plot2d.png\" alt=\"2D Importance\" width=\"80%\">\n</p>\n\n#### Interaction Plots\n\n```python\ninteraction_plot(result2_d, (21, 22))\n```\n\n<p align=\"center\">\n <img src=\"/docs/source/_static/api/interaction_plot.png\" alt=\"Interaction Plot\" width=\"80%\">\n</p>\n\n```python\ninteraction_scatter_plot(X, result2_d, (21, 22))\n```\n\n<p align=\"center\">\n <img src=\"/docs/source/_static/api/interaction_scatter_plot.png\" alt=\"Interaction Scatter\" width=\"80%\">\n</p>\n\n---\n\n\n\n## Contributing\n\nContributions are welcome! If you'd like to improve `treemind` or suggest new features, feel free to fork the repository and submit a pull request.\n\n---\n\n## License\n\n`treemind` is released under the MIT License. See the [LICENSE](./LICENSE) file for details.\n",
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